Overview

Dataset statistics

Number of variables55
Number of observations565892
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory237.5 MiB
Average record size in memory440.0 B

Variable types

Numeric11
Categorical44

Alerts

Id is highly correlated with Wilderness_Area1 and 1 other fieldsHigh correlation
Aspect is highly correlated with Hillshade_3pmHigh correlation
Horizontal_Distance_To_Hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with Horizontal_Distance_To_HydrologyHigh correlation
Hillshade_9am is highly correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly correlated with Hillshade_3pmHigh correlation
Hillshade_3pm is highly correlated with Aspect and 2 other fieldsHigh correlation
Wilderness_Area1 is highly correlated with Id and 2 other fieldsHigh correlation
Wilderness_Area3 is highly correlated with Id and 1 other fieldsHigh correlation
Soil_Type29 is highly correlated with Wilderness_Area1High correlation
Id is highly correlated with Wilderness_Area1 and 1 other fieldsHigh correlation
Elevation is highly correlated with Wilderness_Area4High correlation
Aspect is highly correlated with Hillshade_9am and 1 other fieldsHigh correlation
Slope is highly correlated with Hillshade_NoonHigh correlation
Horizontal_Distance_To_Hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with Horizontal_Distance_To_HydrologyHigh correlation
Hillshade_9am is highly correlated with Aspect and 1 other fieldsHigh correlation
Hillshade_Noon is highly correlated with Slope and 1 other fieldsHigh correlation
Hillshade_3pm is highly correlated with Aspect and 2 other fieldsHigh correlation
Wilderness_Area1 is highly correlated with Id and 2 other fieldsHigh correlation
Wilderness_Area3 is highly correlated with Id and 1 other fieldsHigh correlation
Wilderness_Area4 is highly correlated with ElevationHigh correlation
Soil_Type29 is highly correlated with Wilderness_Area1High correlation
Id is highly correlated with Wilderness_Area1 and 1 other fieldsHigh correlation
Hillshade_9am is highly correlated with Hillshade_3pmHigh correlation
Hillshade_3pm is highly correlated with Hillshade_9amHigh correlation
Wilderness_Area1 is highly correlated with Id and 2 other fieldsHigh correlation
Wilderness_Area3 is highly correlated with Id and 1 other fieldsHigh correlation
Soil_Type29 is highly correlated with Wilderness_Area1High correlation
Wilderness_Area3 is highly correlated with Wilderness_Area1High correlation
Soil_Type29 is highly correlated with Wilderness_Area1High correlation
Wilderness_Area1 is highly correlated with Wilderness_Area3 and 1 other fieldsHigh correlation
Id is highly correlated with Elevation and 8 other fieldsHigh correlation
Elevation is highly correlated with Id and 4 other fieldsHigh correlation
Aspect is highly correlated with Hillshade_9am and 2 other fieldsHigh correlation
Slope is highly correlated with Hillshade_9am and 2 other fieldsHigh correlation
Horizontal_Distance_To_Hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with Horizontal_Distance_To_HydrologyHigh correlation
Horizontal_Distance_To_Roadways is highly correlated with Id and 3 other fieldsHigh correlation
Hillshade_9am is highly correlated with Aspect and 2 other fieldsHigh correlation
Hillshade_Noon is highly correlated with Aspect and 2 other fieldsHigh correlation
Hillshade_3pm is highly correlated with Aspect and 3 other fieldsHigh correlation
Horizontal_Distance_To_Fire_Points is highly correlated with IdHigh correlation
Wilderness_Area1 is highly correlated with Id and 3 other fieldsHigh correlation
Wilderness_Area2 is highly correlated with IdHigh correlation
Wilderness_Area3 is highly correlated with Id and 3 other fieldsHigh correlation
Wilderness_Area4 is highly correlated with Id and 3 other fieldsHigh correlation
Soil_Type6 is highly correlated with Wilderness_Area4High correlation
Soil_Type10 is highly correlated with Elevation and 1 other fieldsHigh correlation
Soil_Type12 is highly correlated with IdHigh correlation
Soil_Type29 is highly correlated with Id and 2 other fieldsHigh correlation
Soil_Type40 is highly correlated with ElevationHigh correlation
Id is uniformly distributed Uniform
Id has unique values Unique
Horizontal_Distance_To_Hydrology has 23013 (4.1%) zeros Zeros
Vertical_Distance_To_Hydrology has 36775 (6.5%) zeros Zeros

Reproduction

Analysis started2022-05-19 20:20:34.420245
Analysis finished2022-05-19 20:24:45.588861
Duration4 minutes and 11.17 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct565892
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298066.5
Minimum15121
Maximum581012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-19T23:24:45.722973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15121
5-th percentile43415.55
Q1156593.75
median298066.5
Q3439539.25
95-th percentile552717.45
Maximum581012
Range565891
Interquartile range (IQR)282945.5

Descriptive statistics

Standard deviation163359.0936
Coefficient of variation (CV)0.5480625753
Kurtosis-1.2
Mean298066.5
Median Absolute Deviation (MAD)141473
Skewness-1.735062171 × 10-15
Sum1.686734478 × 1011
Variance2.668619346 × 1010
MonotonicityStrictly increasing
2022-05-19T23:24:45.874599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151211
 
< 0.1%
3923841
 
< 0.1%
3923781
 
< 0.1%
3923791
 
< 0.1%
3923801
 
< 0.1%
3923811
 
< 0.1%
3923821
 
< 0.1%
3923831
 
< 0.1%
3923851
 
< 0.1%
3923931
 
< 0.1%
Other values (565882)565882
> 99.9%
ValueCountFrequency (%)
151211
< 0.1%
151221
< 0.1%
151231
< 0.1%
151241
< 0.1%
151251
< 0.1%
151261
< 0.1%
151271
< 0.1%
151281
< 0.1%
151291
< 0.1%
151301
< 0.1%
ValueCountFrequency (%)
5810121
< 0.1%
5810111
< 0.1%
5810101
< 0.1%
5810091
< 0.1%
5810081
< 0.1%
5810071
< 0.1%
5810061
< 0.1%
5810051
< 0.1%
5810041
< 0.1%
5810031
< 0.1%

Elevation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1974
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2964.977407
Minimum1859
Maximum3858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-19T23:24:46.155979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1859
5-th percentile2435
Q12818
median2999
Q33164
95-th percentile3333
Maximum3858
Range1999
Interquartile range (IQR)346

Descriptive statistics

Standard deviation273.1570295
Coefficient of variation (CV)0.09212786203
Kurtosis0.824727058
Mean2964.977407
Median Absolute Deviation (MAD)171
Skewness-0.816594243
Sum1677856995
Variance74614.76277
MonotonicityNot monotonic
2022-05-19T23:24:46.307596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29681667
 
0.3%
29911656
 
0.3%
29621652
 
0.3%
29721650
 
0.3%
29751639
 
0.3%
29781634
 
0.3%
29881607
 
0.3%
29551567
 
0.3%
29651565
 
0.3%
29851555
 
0.3%
Other values (1964)549700
97.1%
ValueCountFrequency (%)
18591
 
< 0.1%
18601
 
< 0.1%
18611
 
< 0.1%
18661
 
< 0.1%
18671
 
< 0.1%
18681
 
< 0.1%
18713
< 0.1%
18724
< 0.1%
18731
 
< 0.1%
18762
< 0.1%
ValueCountFrequency (%)
38582
 
< 0.1%
38571
 
< 0.1%
38561
 
< 0.1%
38531
 
< 0.1%
38521
 
< 0.1%
38512
 
< 0.1%
38501
 
< 0.1%
38492
 
< 0.1%
38464
< 0.1%
38456
< 0.1%

Aspect
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct361
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.6295583
Minimum0
Maximum360
Zeros4804
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-19T23:24:46.455683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q158
median127
Q3260
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)202

Descriptive statistics

Standard deviation111.9621199
Coefficient of variation (CV)0.719414237
Kurtosis-1.222069137
Mean155.6295583
Median Absolute Deviation (MAD)85
Skewness0.4014239595
Sum88069522
Variance12535.5163
MonotonicityNot monotonic
2022-05-19T23:24:46.609207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
456191
 
1.1%
04804
 
0.8%
904568
 
0.8%
1353759
 
0.7%
633591
 
0.6%
3153493
 
0.6%
183333
 
0.6%
723330
 
0.6%
273310
 
0.6%
342759
 
0.5%
Other values (351)526754
93.1%
ValueCountFrequency (%)
04804
0.8%
11623
 
0.3%
21852
 
0.3%
31891
 
0.3%
42216
0.4%
52017
0.4%
62185
0.4%
72146
0.4%
82157
0.4%
92409
0.4%
ValueCountFrequency (%)
36049
 
< 0.1%
3591374
0.2%
3581702
0.3%
3571802
0.3%
3561975
0.3%
3551888
0.3%
3541974
0.3%
3531891
0.3%
3521925
0.3%
3512129
0.4%

Slope
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.03963477
Minimum0
Maximum66
Zeros651
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-19T23:24:46.751468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median13
Q318
95-th percentile28
Maximum66
Range66
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.450155458
Coefficient of variation (CV)0.5306516572
Kurtosis0.6092308864
Mean14.03963477
Median Absolute Deviation (MAD)5
Skewness0.7941054537
Sum7944917
Variance55.50481635
MonotonicityNot monotonic
2022-05-19T23:24:46.895933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1133084
 
5.8%
1033073
 
5.8%
1232540
 
5.8%
1331702
 
5.6%
931385
 
5.5%
1429583
 
5.2%
829556
 
5.2%
1528463
 
5.0%
1625901
 
4.6%
725822
 
4.6%
Other values (57)264783
46.8%
ValueCountFrequency (%)
0651
 
0.1%
13602
 
0.6%
27592
 
1.3%
311410
 
2.0%
416039
2.8%
520387
3.6%
624039
4.2%
725822
4.6%
829556
5.2%
931385
5.5%
ValueCountFrequency (%)
661
 
< 0.1%
652
 
< 0.1%
641
 
< 0.1%
631
 
< 0.1%
622
 
< 0.1%
614
< 0.1%
602
 
< 0.1%
593
< 0.1%
581
 
< 0.1%
577
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct551
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean270.5566221
Minimum0
Maximum1397
Zeros23013
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-19T23:24:47.043149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108
median228
Q3390
95-th percentile685
Maximum1397
Range1397
Interquartile range (IQR)282

Descriptive statistics

Standard deviation212.500153
Coefficient of variation (CV)0.785418414
Kurtosis1.338991956
Mean270.5566221
Median Absolute Deviation (MAD)134
Skewness1.133163321
Sum153105828
Variance45156.31504
MonotonicityNot monotonic
2022-05-19T23:24:47.192268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3032932
 
5.8%
023013
 
4.1%
15020288
 
3.6%
6018699
 
3.3%
6714812
 
2.6%
4214195
 
2.5%
10813997
 
2.5%
8513360
 
2.4%
9010856
 
1.9%
12010390
 
1.8%
Other values (541)393350
69.5%
ValueCountFrequency (%)
023013
4.1%
3032932
5.8%
4214195
2.5%
6018699
3.3%
6714812
2.6%
8513360
2.4%
9010856
 
1.9%
958957
 
1.6%
10813997
2.5%
12010390
 
1.8%
ValueCountFrequency (%)
13971
< 0.1%
13902
< 0.1%
13832
< 0.1%
13821
< 0.1%
13761
< 0.1%
13711
< 0.1%
13701
< 0.1%
13691
< 0.1%
13682
< 0.1%
13612
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct700
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.29440777
Minimum-173
Maximum601
Zeros36775
Zeros (%)6.5%
Negative54004
Negative (%)9.5%
Memory size4.3 MiB
2022-05-19T23:24:47.340163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-173
5-th percentile-8
Q17
median29
Q369
95-th percentile165
Maximum601
Range774
Interquartile range (IQR)62

Descriptive statistics

Standard deviation58.20946933
Coefficient of variation (CV)1.257375829
Kurtosis5.310145646
Mean46.29440777
Median Absolute Deviation (MAD)27
Skewness1.797687206
Sum26197635
Variance3388.34232
MonotonicityNot monotonic
2022-05-19T23:24:47.493410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036775
 
6.5%
39092
 
1.6%
108687
 
1.5%
78559
 
1.5%
68428
 
1.5%
138316
 
1.5%
48197
 
1.4%
57397
 
1.3%
167297
 
1.3%
97165
 
1.3%
Other values (690)455979
80.6%
ValueCountFrequency (%)
-1731
 
< 0.1%
-1662
< 0.1%
-1641
 
< 0.1%
-1631
 
< 0.1%
-1611
 
< 0.1%
-1593
< 0.1%
-1581
 
< 0.1%
-1572
< 0.1%
-1562
< 0.1%
-1553
< 0.1%
ValueCountFrequency (%)
6011
 
< 0.1%
5991
 
< 0.1%
5982
< 0.1%
5973
< 0.1%
5952
< 0.1%
5921
 
< 0.1%
5911
 
< 0.1%
5902
< 0.1%
5893
< 0.1%
5883
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5785
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2367.143116
Minimum0
Maximum7117
Zeros121
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-19T23:24:47.646578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile390
Q11116
median2018
Q33349
95-th percentile5494
Maximum7117
Range7117
Interquartile range (IQR)2233

Descriptive statistics

Standard deviation1561.482002
Coefficient of variation (CV)0.6596483294
Kurtosis-0.4027868207
Mean2367.143116
Median Absolute Deviation (MAD)1046
Skewness0.701911129
Sum1339547352
Variance2438226.041
MonotonicityNot monotonic
2022-05-19T23:24:47.795065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1501244
 
0.2%
6181020
 
0.2%
900881
 
0.2%
390867
 
0.2%
1020866
 
0.2%
990841
 
0.1%
960835
 
0.1%
997832
 
0.1%
750813
 
0.1%
1140807
 
0.1%
Other values (5775)556886
98.4%
ValueCountFrequency (%)
0121
 
< 0.1%
30298
0.1%
42166
 
< 0.1%
60301
0.1%
67285
0.1%
85374
0.1%
90357
0.1%
95355
0.1%
108622
0.1%
120577
0.1%
ValueCountFrequency (%)
71171
< 0.1%
71161
< 0.1%
71121
< 0.1%
70971
< 0.1%
70921
< 0.1%
70872
< 0.1%
70821
< 0.1%
70791
< 0.1%
70782
< 0.1%
70691
< 0.1%

Hillshade_9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.1311328
Minimum0
Maximum254
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-19T23:24:47.937312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile160
Q1198
median218
Q3231
95-th percentile246
Maximum254
Range254
Interquartile range (IQR)33

Descriptive statistics

Standard deviation26.661063
Coefficient of variation (CV)0.1256819904
Kurtosis1.894403593
Mean212.1311328
Median Absolute Deviation (MAD)16
Skewness-1.184138141
Sum120043311
Variance710.8122802
MonotonicityNot monotonic
2022-05-19T23:24:48.081623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22611378
 
2.0%
22811113
 
2.0%
23011095
 
2.0%
22410945
 
1.9%
22310642
 
1.9%
22210586
 
1.9%
23310397
 
1.8%
22710291
 
1.8%
22510071
 
1.8%
22110033
 
1.8%
Other values (197)459341
81.2%
ValueCountFrequency (%)
012
< 0.1%
361
 
< 0.1%
462
 
< 0.1%
501
 
< 0.1%
522
 
< 0.1%
531
 
< 0.1%
544
 
< 0.1%
551
 
< 0.1%
566
< 0.1%
572
 
< 0.1%
ValueCountFrequency (%)
2541708
 
0.3%
2532036
 
0.4%
2522374
0.4%
2512794
0.5%
2503149
0.6%
2493598
0.6%
2483777
0.7%
2474255
0.8%
2464827
0.9%
2455329
0.9%

Hillshade_Noon
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct185
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.4350265
Minimum0
Maximum254
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-19T23:24:48.352326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile187
Q1213
median226
Q3237
95-th percentile250
Maximum254
Range254
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.66805273
Coefficient of variation (CV)0.08802582585
Kurtosis2.087614542
Mean223.4350265
Median Absolute Deviation (MAD)12
Skewness-1.062229916
Sum126440094
Variance386.8322982
MonotonicityNot monotonic
2022-05-19T23:24:48.504916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22813402
 
2.4%
23113382
 
2.4%
23313036
 
2.3%
23012947
 
2.3%
22912947
 
2.3%
23412778
 
2.3%
22712731
 
2.2%
22312686
 
2.2%
22612633
 
2.2%
22512601
 
2.2%
Other values (175)436749
77.2%
ValueCountFrequency (%)
05
< 0.1%
301
 
< 0.1%
401
 
< 0.1%
421
 
< 0.1%
451
 
< 0.1%
532
 
< 0.1%
631
 
< 0.1%
641
 
< 0.1%
681
 
< 0.1%
711
 
< 0.1%
ValueCountFrequency (%)
2545769
1.0%
2536137
1.1%
2527019
1.2%
2517288
1.3%
2507861
1.4%
2497538
1.3%
2487937
1.4%
2478664
1.5%
2468451
1.5%
2458331
1.5%

Hillshade_3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct255
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.7269514
Minimum0
Maximum254
Zeros1250
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-19T23:24:48.649971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78
Q1119
median143
Q3168
95-th percentile204
Maximum254
Range254
Interquartile range (IQR)49

Descriptive statistics

Standard deviation38.03009388
Coefficient of variation (CV)0.2664534869
Kurtosis0.398717465
Mean142.7269514
Median Absolute Deviation (MAD)24
Skewness-0.2666220506
Sum80768040
Variance1446.288041
MonotonicityNot monotonic
2022-05-19T23:24:48.806494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1437151
 
1.3%
1457087
 
1.3%
1386917
 
1.2%
1466786
 
1.2%
1426748
 
1.2%
1396740
 
1.2%
1366717
 
1.2%
1356643
 
1.2%
1496562
 
1.2%
1326517
 
1.2%
Other values (245)498024
88.0%
ValueCountFrequency (%)
01250
0.2%
114
 
< 0.1%
215
 
< 0.1%
312
 
< 0.1%
419
 
< 0.1%
518
 
< 0.1%
624
 
< 0.1%
729
 
< 0.1%
820
 
< 0.1%
931
 
< 0.1%
ValueCountFrequency (%)
2544
 
< 0.1%
2538
 
< 0.1%
25216
 
< 0.1%
25111
 
< 0.1%
25017
 
< 0.1%
24937
< 0.1%
24842
< 0.1%
24757
< 0.1%
24668
< 0.1%
24581
< 0.1%

Horizontal_Distance_To_Fire_Points
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5826
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1992.826227
Minimum0
Maximum7173
Zeros49
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2022-05-19T23:24:48.956985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile421
Q11034
median1723
Q32561
95-th percentile4966
Maximum7173
Range7173
Interquartile range (IQR)1527

Descriptive statistics

Standard deviation1327.396895
Coefficient of variation (CV)0.6660876283
Kurtosis1.615323198
Mean1992.826227
Median Absolute Deviation (MAD)751
Skewness1.281245497
Sum1127724419
Variance1761982.517
MonotonicityNot monotonic
2022-05-19T23:24:49.114317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6181347
 
0.2%
5411048
 
0.2%
6071011
 
0.2%
942983
 
0.2%
997969
 
0.2%
700921
 
0.2%
900907
 
0.2%
726887
 
0.2%
1082874
 
0.2%
752869
 
0.2%
Other values (5816)556076
98.3%
ValueCountFrequency (%)
049
 
< 0.1%
30197
< 0.1%
42196
< 0.1%
60196
< 0.1%
67396
0.1%
85199
< 0.1%
90195
< 0.1%
95393
0.1%
108387
0.1%
120196
< 0.1%
ValueCountFrequency (%)
71731
< 0.1%
71721
< 0.1%
71681
< 0.1%
71501
< 0.1%
71451
< 0.1%
71421
< 0.1%
71412
< 0.1%
71401
< 0.1%
71311
< 0.1%
71261
< 0.1%

Wilderness_Area1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
308693 
1
257199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0308693
54.5%
1257199
45.5%

Length

2022-05-19T23:24:49.249954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:49.395852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0308693
54.5%
1257199
45.5%

Most occurring characters

ValueCountFrequency (%)
0308693
54.5%
1257199
45.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0308693
54.5%
1257199
45.5%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0308693
54.5%
1257199
45.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0308693
54.5%
1257199
45.5%

Wilderness_Area2
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
536507 
1
 
29385

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0536507
94.8%
129385
 
5.2%

Length

2022-05-19T23:24:49.519885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:49.652565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0536507
94.8%
129385
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0536507
94.8%
129385
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0536507
94.8%
129385
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0536507
94.8%
129385
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0536507
94.8%
129385
 
5.2%

Wilderness_Area3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
318877 
1
247015 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0318877
56.3%
1247015
43.7%

Length

2022-05-19T23:24:49.767814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:49.906948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0318877
56.3%
1247015
43.7%

Most occurring characters

ValueCountFrequency (%)
0318877
56.3%
1247015
43.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0318877
56.3%
1247015
43.7%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0318877
56.3%
1247015
43.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0318877
56.3%
1247015
43.7%

Wilderness_Area4
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
533599 
1
 
32293

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0533599
94.3%
132293
 
5.7%

Length

2022-05-19T23:24:50.028622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:50.158277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0533599
94.3%
132293
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0533599
94.3%
132293
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0533599
94.3%
132293
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0533599
94.3%
132293
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0533599
94.3%
132293
 
5.7%

Soil_Type1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
563216 
1
 
2676

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0563216
99.5%
12676
 
0.5%

Length

2022-05-19T23:24:50.266948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:50.385203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0563216
99.5%
12676
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0563216
99.5%
12676
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0563216
99.5%
12676
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0563216
99.5%
12676
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0563216
99.5%
12676
 
0.5%

Soil_Type2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
558990 
1
 
6902

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0558990
98.8%
16902
 
1.2%

Length

2022-05-19T23:24:50.499657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:50.637134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0558990
98.8%
16902
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0558990
98.8%
16902
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0558990
98.8%
16902
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0558990
98.8%
16902
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0558990
98.8%
16902
 
1.2%

Soil_Type3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
562031 
1
 
3861

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0562031
99.3%
13861
 
0.7%

Length

2022-05-19T23:24:50.755259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:50.879355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0562031
99.3%
13861
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0562031
99.3%
13861
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0562031
99.3%
13861
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0562031
99.3%
13861
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0562031
99.3%
13861
 
0.7%

Soil_Type4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
554339 
1
 
11553

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0554339
98.0%
111553
 
2.0%

Length

2022-05-19T23:24:50.986092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:51.243179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0554339
98.0%
111553
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0554339
98.0%
111553
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0554339
98.0%
111553
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0554339
98.0%
111553
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0554339
98.0%
111553
 
2.0%

Soil_Type5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
564460 
1
 
1432

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0564460
99.7%
11432
 
0.3%

Length

2022-05-19T23:24:51.376819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:51.520951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0564460
99.7%
11432
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0564460
99.7%
11432
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0564460
99.7%
11432
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0564460
99.7%
11432
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0564460
99.7%
11432
 
0.3%

Soil_Type6
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
559967 
1
 
5925

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0559967
99.0%
15925
 
1.0%

Length

2022-05-19T23:24:51.661608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:51.794219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0559967
99.0%
15925
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0559967
99.0%
15925
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0559967
99.0%
15925
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0559967
99.0%
15925
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0559967
99.0%
15925
 
1.0%

Soil_Type7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
565787 
1
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0565787
> 99.9%
1105
 
< 0.1%

Length

2022-05-19T23:24:51.920881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:52.050533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0565787
> 99.9%
1105
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0565787
> 99.9%
1105
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0565787
> 99.9%
1105
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0565787
> 99.9%
1105
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0565787
> 99.9%
1105
 
< 0.1%

Soil_Type8
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
565714 
1
 
178

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0565714
> 99.9%
1178
 
< 0.1%

Length

2022-05-19T23:24:52.163375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:52.304425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0565714
> 99.9%
1178
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0565714
> 99.9%
1178
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0565714
> 99.9%
1178
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0565714
> 99.9%
1178
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0565714
> 99.9%
1178
 
< 0.1%

Soil_Type9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
564755 
1
 
1137

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0564755
99.8%
11137
 
0.2%

Length

2022-05-19T23:24:52.440062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:52.578722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0564755
99.8%
11137
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0564755
99.8%
11137
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0564755
99.8%
11137
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0564755
99.8%
11137
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0564755
99.8%
11137
 
0.2%

Soil_Type10
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
535400 
1
 
30492

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0535400
94.6%
130492
 
5.4%

Length

2022-05-19T23:24:52.721879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:52.887408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0535400
94.6%
130492
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0535400
94.6%
130492
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0535400
94.6%
130492
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0535400
94.6%
130492
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0535400
94.6%
130492
 
5.4%

Soil_Type11
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
553888 
1
 
12004

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0553888
97.9%
112004
 
2.1%

Length

2022-05-19T23:24:53.042027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:53.193622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0553888
97.9%
112004
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0553888
97.9%
112004
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0553888
97.9%
112004
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0553888
97.9%
112004
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0553888
97.9%
112004
 
2.1%

Soil_Type12
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
536148 
1
 
29744

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0536148
94.7%
129744
 
5.3%

Length

2022-05-19T23:24:53.329259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:53.473461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0536148
94.7%
129744
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0536148
94.7%
129744
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0536148
94.7%
129744
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0536148
94.7%
129744
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0536148
94.7%
129744
 
5.3%

Soil_Type13
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
548937 
1
 
16955

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0548937
97.0%
116955
 
3.0%

Length

2022-05-19T23:24:53.608108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:53.751724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0548937
97.0%
116955
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0548937
97.0%
116955
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0548937
97.0%
116955
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0548937
97.0%
116955
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0548937
97.0%
116955
 
3.0%

Soil_Type14
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
565462 
1
 
430

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0565462
99.9%
1430
 
0.1%

Length

2022-05-19T23:24:53.893345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:54.048474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0565462
99.9%
1430
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0565462
99.9%
1430
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0565462
99.9%
1430
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0565462
99.9%
1430
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0565462
99.9%
1430
 
0.1%

Soil_Type15
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
565889 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0565889
> 99.9%
13
 
< 0.1%

Length

2022-05-19T23:24:54.161710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:54.282058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0565889
> 99.9%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0565889
> 99.9%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0565889
> 99.9%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0565889
> 99.9%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0565889
> 99.9%
13
 
< 0.1%

Soil_Type16
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
563161 
1
 
2731

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0563161
99.5%
12731
 
0.5%

Length

2022-05-19T23:24:54.398776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:54.523450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0563161
99.5%
12731
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0563161
99.5%
12731
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0563161
99.5%
12731
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0563161
99.5%
12731
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0563161
99.5%
12731
 
0.5%

Soil_Type17
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
563082 
1
 
2810

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0563082
99.5%
12810
 
0.5%

Length

2022-05-19T23:24:54.638145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:54.905206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0563082
99.5%
12810
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0563082
99.5%
12810
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0563082
99.5%
12810
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0563082
99.5%
12810
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0563082
99.5%
12810
 
0.5%

Soil_Type18
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
564053 
1
 
1839

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0564053
99.7%
11839
 
0.3%

Length

2022-05-19T23:24:55.022566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:55.157573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0564053
99.7%
11839
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0564053
99.7%
11839
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0564053
99.7%
11839
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0564053
99.7%
11839
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0564053
99.7%
11839
 
0.3%

Soil_Type19
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
561917 
1
 
3975

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0561917
99.3%
13975
 
0.7%

Length

2022-05-19T23:24:55.286227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:55.401979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0561917
99.3%
13975
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0561917
99.3%
13975
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0561917
99.3%
13975
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0561917
99.3%
13975
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0561917
99.3%
13975
 
0.7%

Soil_Type20
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
556772 
1
 
9120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0556772
98.4%
19120
 
1.6%

Length

2022-05-19T23:24:55.510008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:55.624082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0556772
98.4%
19120
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0556772
98.4%
19120
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0556772
98.4%
19120
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0556772
98.4%
19120
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0556772
98.4%
19120
 
1.6%

Soil_Type21
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
565070 
1
 
822

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0565070
99.9%
1822
 
0.1%

Length

2022-05-19T23:24:55.731237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:55.844224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0565070
99.9%
1822
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0565070
99.9%
1822
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0565070
99.9%
1822
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0565070
99.9%
1822
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0565070
99.9%
1822
 
0.1%

Soil_Type22
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
532864 
1
 
33028

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0532864
94.2%
133028
 
5.8%

Length

2022-05-19T23:24:55.951815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:56.080319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0532864
94.2%
133028
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0532864
94.2%
133028
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0532864
94.2%
133028
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0532864
94.2%
133028
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0532864
94.2%
133028
 
5.8%

Soil_Type23
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
508897 
1
56995 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0508897
89.9%
156995
 
10.1%

Length

2022-05-19T23:24:56.228923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:56.361595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0508897
89.9%
156995
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0508897
89.9%
156995
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0508897
89.9%
156995
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0508897
89.9%
156995
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0508897
89.9%
156995
 
10.1%

Soil_Type24
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
544871 
1
 
21021

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0544871
96.3%
121021
 
3.7%

Length

2022-05-19T23:24:56.468996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:56.585643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0544871
96.3%
121021
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0544871
96.3%
121021
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0544871
96.3%
121021
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0544871
96.3%
121021
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0544871
96.3%
121021
 
3.7%

Soil_Type25
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
565419 
1
 
473

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0565419
99.9%
1473
 
0.1%

Length

2022-05-19T23:24:56.694369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:56.812639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0565419
99.9%
1473
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0565419
99.9%
1473
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0565419
99.9%
1473
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0565419
99.9%
1473
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0565419
99.9%
1473
 
0.1%

Soil_Type26
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
563357 
1
 
2535

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0563357
99.6%
12535
 
0.4%

Length

2022-05-19T23:24:56.922346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:57.057965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0563357
99.6%
12535
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0563357
99.6%
12535
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0563357
99.6%
12535
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0563357
99.6%
12535
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0563357
99.6%
12535
 
0.4%

Soil_Type27
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
564821 
1
 
1071

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0564821
99.8%
11071
 
0.2%

Length

2022-05-19T23:24:57.197131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:57.363685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0564821
99.8%
11071
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0564821
99.8%
11071
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0564821
99.8%
11071
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0564821
99.8%
11071
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0564821
99.8%
11071
 
0.2%

Soil_Type28
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
564955 
1
 
937

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0564955
99.8%
1937
 
0.2%

Length

2022-05-19T23:24:57.494839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:57.644438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0564955
99.8%
1937
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0564955
99.8%
1937
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0564955
99.8%
1937
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0564955
99.8%
1937
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0564955
99.8%
1937
 
0.2%

Soil_Type29
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
451936 
1
113956 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0451936
79.9%
1113956
 
20.1%

Length

2022-05-19T23:24:57.772097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:57.894769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0451936
79.9%
1113956
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0451936
79.9%
1113956
 
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0451936
79.9%
1113956
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0451936
79.9%
1113956
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0451936
79.9%
1113956
 
20.1%

Soil_Type30
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
536447 
1
 
29445

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0536447
94.8%
129445
 
5.2%

Length

2022-05-19T23:24:58.008110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:58.254497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0536447
94.8%
129445
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0536447
94.8%
129445
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0536447
94.8%
129445
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0536447
94.8%
129445
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0536447
94.8%
129445
 
5.2%

Soil_Type31
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
540558 
1
 
25334

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0540558
95.5%
125334
 
4.5%

Length

2022-05-19T23:24:58.376756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:58.528356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0540558
95.5%
125334
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0540558
95.5%
125334
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0540558
95.5%
125334
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0540558
95.5%
125334
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0540558
95.5%
125334
 
4.5%

Soil_Type32
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
514063 
1
51829 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0514063
90.8%
151829
 
9.2%

Length

2022-05-19T23:24:58.661995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:58.802420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0514063
90.8%
151829
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0514063
90.8%
151829
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0514063
90.8%
151829
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0514063
90.8%
151829
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0514063
90.8%
151829
 
9.2%

Soil_Type33
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
521354 
1
 
44538

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0521354
92.1%
144538
 
7.9%

Length

2022-05-19T23:24:58.936029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:59.077681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0521354
92.1%
144538
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0521354
92.1%
144538
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0521354
92.1%
144538
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0521354
92.1%
144538
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0521354
92.1%
144538
 
7.9%

Soil_Type34
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
564303 
1
 
1589

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0564303
99.7%
11589
 
0.3%

Length

2022-05-19T23:24:59.204313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:59.351916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0564303
99.7%
11589
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0564303
99.7%
11589
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0564303
99.7%
11589
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0564303
99.7%
11589
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0564303
99.7%
11589
 
0.3%

Soil_Type35
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
564103 
1
 
1789

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0564103
99.7%
11789
 
0.3%

Length

2022-05-19T23:24:59.482569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:59.629213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0564103
99.7%
11789
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0564103
99.7%
11789
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0564103
99.7%
11789
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0564103
99.7%
11789
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0564103
99.7%
11789
 
0.3%

Soil_Type36
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
565783 
1
 
109

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0565783
> 99.9%
1109
 
< 0.1%

Length

2022-05-19T23:24:59.757830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:24:59.893125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0565783
> 99.9%
1109
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0565783
> 99.9%
1109
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0565783
> 99.9%
1109
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0565783
> 99.9%
1109
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0565783
> 99.9%
1109
 
< 0.1%

Soil_Type37
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
565628 
1
 
264

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0565628
> 99.9%
1264
 
< 0.1%

Length

2022-05-19T23:25:00.025976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:25:00.167597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0565628
> 99.9%
1264
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0565628
> 99.9%
1264
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0565628
> 99.9%
1264
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0565628
> 99.9%
1264
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0565628
> 99.9%
1264
 
< 0.1%

Soil_Type38
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
551047 
1
 
14845

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0551047
97.4%
114845
 
2.6%

Length

2022-05-19T23:25:00.295251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:25:00.421533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0551047
97.4%
114845
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0551047
97.4%
114845
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0551047
97.4%
114845
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0551047
97.4%
114845
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0551047
97.4%
114845
 
2.6%

Soil_Type39
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
552743 
1
 
13149

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0552743
97.7%
113149
 
2.3%

Length

2022-05-19T23:25:00.534276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:25:00.653812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0552743
97.7%
113149
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0552743
97.7%
113149
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0552743
97.7%
113149
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0552743
97.7%
113149
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0552743
97.7%
113149
 
2.3%

Soil_Type40
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
0
557601 
1
 
8291

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters565892
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0557601
98.5%
18291
 
1.5%

Length

2022-05-19T23:25:00.772537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-19T23:25:00.900588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0557601
98.5%
18291
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0557601
98.5%
18291
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number565892
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0557601
98.5%
18291
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common565892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0557601
98.5%
18291
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII565892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0557601
98.5%
18291
 
1.5%

Interactions

2022-05-19T23:24:39.284517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:08.028490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:11.040474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:14.223564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:17.438556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:20.695875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:23.816579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:26.961379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:29.954349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:33.141776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:36.144954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:39.544583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:08.316108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:11.297196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:14.509008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:17.718346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:20.976223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:24.098054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:27.221853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:30.222422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:33.410599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:36.416357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:39.811597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:08.582685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:11.581761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:14.792916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:18.005141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:21.259489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:24.370983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:27.498364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:30.512691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:33.682302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:36.702145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:40.082639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:08.851970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:11.849086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:15.084814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:18.298409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:21.555695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:24.650269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:27.773037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:30.829732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:33.954298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:36.976261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:40.347919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:09.122522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:12.119229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:15.366091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:18.585636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:21.840501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:24.921430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:28.044014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:31.145868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:34.219803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:37.245148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:40.612722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:09.389818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:12.399077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:15.652748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:18.872071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:22.121519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:25.192556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:28.325229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:31.425829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:34.515979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:37.516834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:40.878217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:09.668472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:12.711210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:15.943603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:19.159302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:22.403336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:25.465303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:28.615802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:31.696612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:34.793646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:37.805468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:41.151680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:09.953708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:12.991353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:16.248787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:19.452354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:22.686571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:25.752341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:28.885379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:31.964235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:35.061385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:38.086091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:41.414932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:10.230966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:13.265916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:16.561977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:19.731681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:22.975796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:26.032591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:29.151897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:32.235106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:35.327397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:38.358047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:41.691051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:10.506213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:13.651884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:16.850447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:20.127394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:23.260038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:26.427728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:29.419154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:32.509488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:35.612580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:38.631775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:42.013186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:10.778391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:13.935327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:17.148617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:20.410637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:23.540285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:26.693071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:29.677924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:32.878631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:35.876421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-19T23:24:38.917598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-19T23:25:01.096065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-19T23:25:01.744513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-19T23:25:02.312523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-19T23:25:02.802244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-19T23:25:03.129398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-19T23:24:43.414193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

IdElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40
015121268035414002684196214156664510000000000000000000000000000000100000000000
1151222683013002654201216152667510000000000000000000000000000000100000000000
21512327131615002980206208137634410000000000000000000000000000000100000000000
31512427092417002950208201125637410000000000000000000000000000000100000000000
41512527062919002920210195115640410000000000000000000000000000000100000000000
515126269921183032890206200127643410000000000000000000000000000000100000000000
615127269915173062860202202133646410000000000000000000000000000000100000000000
715128269610163062830202207140649410000000000000000000000000000000100000000000
815129269617133062770208211138655310000000000000000000000000000000100000000000
915130269314153072741205209138658310000000000000000000000000000000100000000000

Last rows

IdElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40
5658825810032419168251083312423024012681200100100000000000000000000000000000000000000
565883581004241516125952912023623711681500100100000000000000000000000000000000000000
565884581005241015824902412023823611581900100100000000000000000000000000000000000000
565885581006240515922901912023723811982400100100000000000000000000000000000000000000
565886581007240115721901512023823811983000100100000000000000000000000000000000000000
565887581008239615320851710824023711883700100100000000000000000000000000000000000000
56588858100923911521967129524023711984500100100000000000000000000000000000000000000
5658895810102386159176079023624113085400100100000000000000000000000000000000000000
5658905810112384170156059023024514386400100100000000000000000000000000000000000000
5658915810122383165136046723124414187500100100000000000000000000000000000000000000